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Daniel Reker

Assistant Professor of Biomedical Engineering
Biomedical Engineering

Selected Publications


Taking a deep dive with active learning for drug discovery.

Journal Article Nature computational science · October 2024 Full text Cite

Yoked learning in molecular data science

Journal Article Artificial Intelligence in the Life Sciences · June 1, 2024 Active machine learning is an established and increasingly popular experimental design technique where the machine learning model can request additional data to improve the model's predictive performance. It is generally assumed that this data is optimal f ... Full text Cite

A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research.

Journal Article Nature nanotechnology · June 2024 Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a compre ... Full text Cite

The landscape of small-molecule prodrugs.

Journal Article Nat Rev Drug Discov · May 2024 Prodrugs are derivatives with superior properties compared with the parent active pharmaceutical ingredient (API), which undergo biotransformation after administration to generate the API in situ. Although sharing this general characteristic, prodrugs enco ... Full text Link to item Cite

Silk Fibroin-Based Coatings for Pancreatin-Dependent Drug Delivery.

Journal Article Journal of pharmaceutical sciences · March 2024 Triggerable coatings, such as pH-responsive polymethacrylate copolymers, can be used to protect the active pharmaceutical ingredients contained within oral solid dosage forms from the acidic gastric environment and to facilitate drug delivery directly to t ... Full text Cite

Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning.

Journal Article Nature biomedical engineering · March 2024 In vitro systems that accurately model in vivo conditions in the gastrointestinal tract may aid the development of oral drugs with greater bioavailability. Here we show that the interaction profiles between drugs and intestinal drug transporters can be obt ... Full text Cite

Characterizing emerging companies in computational drug development.

Journal Article Nature computational science · February 2024 Computation promises to accelerate, de-risk and optimize drug research and development. An increasing number of companies have entered this space, specializing in the design of new algorithms, computing on proprietary data, and/or development of hardware t ... Full text Cite

Finding the most potent compounds using active learning on molecular pairs.

Journal Article Beilstein journal of organic chemistry · January 2024 Active learning allows algorithms to steer iterative experimentation to accelerate and de-risk molecular optimizations, but actively trained models might still exhibit poor performance during early project stages where the training data is limited and mode ... Full text Cite

Artificial intelligence for natural product drug discovery.

Journal Article Nature reviews. Drug discovery · November 2023 Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to e ... Full text Cite

DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning.

Journal Article Journal of cheminformatics · October 2023 Established molecular machine learning models process individual molecules as inputs to predict their biological, chemical, or physical properties. However, such algorithms require large datasets and have not been optimized to predict property differences ... Full text Cite

Improving molecular machine learning through adaptive subsampling with active learning

Journal Article Digital Discovery · August 1, 2023 Data subsampling is an established machine learning pre-processing technique to reduce bias in datasets. However, subsampling can lead to the removal of crucial information from the data and thereby decrease performance. Multiple different subsampling stra ... Full text Cite

Interpretable Molecular Property Predictions Using Marginalized Graph Kernels.

Journal Article Journal of chemical information and modeling · August 2023 Marginalized graph kernels have shown competitive performance in molecular machine learning tasks but currently lack measures of interpretability, which are important to improve trust in the models, detect biases, and inform molecular optimization campaign ... Full text Cite

Oral mRNA delivery using capsule-mediated gastrointestinal tissue injections

Journal Article Matter · March 2, 2022 Nucleic acids are enabling a new generation of therapeutics and vaccines to treat and prevent a range of diseases. While these therapies have typically been limited to parenteral dosing, patients and clinicians prefer oral dosage forms. Furthermore, oral d ... Full text Cite

Dynamic Monitoring of Systemic Biomarkers with Gastric Sensors.

Journal Article Advanced science (Weinheim, Baden-Wurttemberg, Germany) · December 2021 Continuous monitoring in the intensive care setting has transformed the capacity to rapidly respond with interventions for patients in extremis. Noninvasive monitoring has generally been limited to transdermal or intravascular systems coupled to transducer ... Full text Cite